从 TCGA 三项全局数据--变异、表达、甲基化--得出与肿瘤反应率相关的因素。

IF 1.2 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Journal of Environmental Science and Health Part C-Toxicology and Carcinogenesis Pub Date : 2024-01-01 Epub Date: 2024-02-26 DOI:10.1080/26896583.2024.2319010
Hyung-Min Ahn, Insu Park, Chang Geun Kim, Young Kyung Ko, Jeong-An Gim
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引用次数: 0

摘要

癌症基因组图谱(TCGA)及其源自患者的多组学数据集一直是癌症研究的中坚力量,通过新方法,它不断揭示出癌症的新见解。在这项研究中,我们深入研究了患者数据集的多组学整合方法以及与疾病相关的生物通路的关联。首先,在TCGA中的33种癌症中,我们合并了基因组突变和药物反应数据集,筛选出TCGA中可行的变异-药物反应组合,每个药物反应标签包含三个以上样本,每个患者都有RNA测序(RNA-seq)和基因组甲基化数据。我们在 TCGA 中发现了两种不同的组合,一种是 KRAS 基因中含有/不含有 rs121913529 变异的胰腺癌患者接受吉西他滨治疗,另一种是 IDH1 基因中含有/不含有 rs121913500 变异的低级别胶质瘤患者接受替莫唑胺治疗。在这两组患者中,观察到完全反应患者和进展期患者在通常与癌症进展相关的通路(如 mTOR 和 PDGF)上有不同的基因表达模式。我们的研究结果将再次证明这些生物通路与癌症药物反应的相关性,并为癌症数据集的多组学整合提供一种方法。
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Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation.

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

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